Chronic fatigue syndrome is a disabling condition for 2.5 million Americans associated with prolonged fatigue, post-exertional malaise, and sleep disturbances. The cause of CFS remains unknown, and there are currently no available diagnostic tests to confirm disease. However, a substantial number of immune features have been measured in CFS. Given this problem, the objective of this project is to identify a panel of distinct antibody epitopes that can be used to identify cases of CFS using a blood test. In Aim 1, we will apply serum epitope repertoire analysis to determine the antibody epitope repertoires within a cohort of 200 CFS patients and 175 controls. Antibody epitopes occurring in CFS sera, and not controls will be identified using bioinformatics methods. A panel of motifs will be downselected using machine learning to optimize sensitivity and specificity within the discovery set. The performance of the panel will be measured in an independent set of specimens to determine sensitivity and specificity, and identify potential CFS subgroups. We will determine whether individual markers, or sets of markers, correlate with various clinical features of CFS. Infections with a variety of pathogens have been associated with the development of fatigue lasting one year or longer, and post-infection fatigue patients typically meet clinical criteria for CFS. Given the potential for heterogeneous infectious etiology, we hypothesize that CFS patients may exhibit increased rates of seropositivity for a broad set of infectious agents associated with fatigue. Using serum epitope repertoire analysis, we will determine whether IgG seropositivity for 20 infections (as a group) differs between CFS patients and controls. The proposed project may identify antibody biomarkers suitable for development of diagnostic immunoassay for CFS, and may elucidate whether prior or ongoing infections are associated with CFS.